Analyse appartements - Shapash

Interpretation des predictions appartements

Project_Information

Author : VotreNom

Description : Rapport Shapash pour appartements

Project_Name : Analyse randomforest_appart


Model analysis

Model used : RandomForestRegressor

Library : sklearn.ensemble._forest

Library version : 1.5.2

Model parameters :

Parameter key Parameter value
estimator DecisionTreeRegressor()
n_estimators 257
estimator_params ('criterion', 'max_depth', 'min_samples_split', 'min_samples_leaf', 'min_weight_fraction_leaf', 'max_features', 'max_leaf_nodes', 'min_impurity_decrease', 'random_state', 'ccp_alpha', 'monotonic_cst')
bootstrap True
oob_score False
n_jobs None
random_state None
verbose 0
warm_start False
class_weight None
max_samples None
criterion squared_error
max_depth 18
min_samples_split 4
Parameter key Parameter value
min_samples_leaf 2
min_weight_fraction_leaf 0.0
max_features 1.0
max_leaf_nodes None
min_impurity_decrease 0.0
ccp_alpha 0.0
monotonic_cst None
feature_names_in_ ['etage' 'surface' 'nb_pieces' 'balcon' 'eau' 'bain' 'dpeL' 'dpeC' 'mapCoordonneesLatitude' 'mapCoordonneesLongitude' 'annonce_exclusive' 'nb_etages' 'places_parking' 'cave' 'ges_class' 'annee_construction' 'nb_toilettes' 'ascenseur' 'nb_logements_copro' 'charges_copro' 'chauffage_energie' 'chauffage_systeme'...
n_features_in_ 56
_n_samples 11035
n_outputs_ 1
_n_samples_bootstrap 11035
estimator_ DecisionTreeRegressor()
estimators_ [DecisionTreeRegressor(max_depth=18, max_features=1.0, min_samples_leaf=2, min_samples_split=4, random_state=2001825433), DecisionTreeRegressor(max_depth=18, max_features=1.0, min_samples_leaf=2, min_samples_split=4, random_state=450656949), DecisionTreeRegressor(max_depth=18, max_features=1.0,...

Dataset analysis

Global analysis

Training dataset Prediction dataset
number of features NaN 56
number of observations NaN 2,759
missing values NaN 0
% missing values NaN 0

Univariate analysis

etage - Numeric

Prediction dataset
count 2,759
mean -0.0135
std 0.976
min -0.514
25% -0.514
50% -0.155
75% 0.205
max 17.5

Target analysis

prix_m2_vente - Numeric

Prediction dataset
count 2,759
mean 2,550
std 1,070
min 216
25% 1,710
50% 2,440
75% 3,300
max 7,440

Multivariate analysis


Model explainability

Note : the explainability graphs were generated using the test set only.

Global feature importance plot

Features contribution plots

etage -


Model performance

Univariate analysis of target variable

prix_m2_vente - Numeric

True values Prediction values
count 2,759 2,759
mean 2,550 2,580
std 1,070 870
min 216 858
25% 1,710 1,880
50% 2,440 2,470
75% 3,300 3,190
max 7,440 7,050

Metrics

MAE : 375

R2 : 0.755

MSE : 283,000

MAPE : 0.189

MdAE : 274

Explained Variance : 0.756